{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6885f14b",
   "metadata": {},
   "source": [
    "## 测试python语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ef88447",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3f2b6e",
   "metadata": {},
   "source": [
    "# 测试导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43bc9e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86907da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tytyty = 10"
   ]
  },
  {
   "source": [
    "![](image/1.png)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.path import Path\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "verts = [\n",
    "    (0., 0.),  # P0\n",
    "    (0.2, 1.), # P1\n",
    "    (1., 0.8), # P2\n",
    "    (0.8, 0.), # P3\n",
    "    ]\n",
    "\n",
    "codes = [Path.MOVETO,\n",
    "         Path.CURVE4,\n",
    "         Path.CURVE4,\n",
    "         Path.CURVE4,\n",
    "         ]\n",
    "\n",
    "path = Path(verts, codes)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "patch = patches.PathPatch(path, facecolor='none', lw=2)\n",
    "ax.add_patch(patch)\n",
    "\n",
    "xs, ys = zip(*verts)\n",
    "ax.plot(xs, ys, 'x--', lw=2, color='black', ms=10)\n",
    "\n",
    "ax.text(-0.05, -0.05, 'P0')\n",
    "ax.text(0.15, 1.05, 'P1')\n",
    "ax.text(1.05, 0.85, 'P2')\n",
    "ax.text(0.85, -0.05, 'P3')\n",
    "\n",
    "ax.set_xlim(-0.1, 1.1)\n",
    "ax.set_ylim(-0.1, 1.1)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "![](image/2.png)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.8 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "interpreter": {
   "hash": "b3bc399b0814b40a0ba5bd9df4237b58abe7d951190c4f8596bfacaccf729cb8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}